Keywords: Vascular cognitive impairment; Vascular dementia; Guidelines; Criteria; . Sensitivity to subtypes. The O'Brien construct was thought by 31 % of. Care should be taken to identify Lewy body dementia, because of the risk of .. what new problems the family has to cope with. Physical examination. Cognitive impairment and dementia may occur from vascular brain injury alone. .. Co-existence of multiple types of VBI in the same brain. The overall.
Vascular dementia 3.2.2.
Department of Health, Optimising treatment and care for people with behavioural and psychological symptoms of dementia: A best practice guide for health and social care professionals. Principal risks and risk-reduction measures 3. Precautions and risks associated with special uses of antipsychotics 4. Summary of licensed indications of antipsychotics for adults 8.
Classification of antipsychotics 9. Structural formulae of selected antipsychotics Feedback on self-assessment questions Further reading on this topic: Page 33 of 46 First Back Forward Last. Eachfeature set was added in isolation i. Quadratic cross terms were added to each feature set, but resulted in worse perfor-mance for strips, quarters and discourse. Thus these results do not use quadraticterms for those sets.
Adding halves features improve the F-measure of the best clas-sifier from 0. The strips set has the second largest improvement to logistic regression, improvingthe F-measure to 0. Quarters and discourse had anegligible effect on most models. Theeffect of the halves features on the suboptimal models is mixed: However, when the the quadratic terms are removed, theperformance of Random Forests and Gaussian Naive Bayes is no longer decreasedby the inclusion of halves features.
A plot of the change in performance withoutquadratic features is shown in supplemental material, as are plots for AUC andaccuracy with the new features. Last we see how the feature score for halves compare to other features in Fig-ure 4. The highest scoring feature across all features is perception: Respondents with dementia are less perceptive on their rightside than healthy controls, they use more pronouns and shorter words on average,and they are older.
We showedthat by partitioning the CookieTheft image in two halves and measuring four sim-ple metrics of spatial neglect attention, concentration, repetition, and perception plus their quadratic cross terms , we improve the F-measure of the best classi-fier, logistic regression, by 2.
One spatial neglect feature, Perception: Rightside, was more highlycorrelated with a dementia diagnosis than all other features, including age. Im-provements were seen in a number of models, although the addition of quadraticcross terms hurt some suboptimal models. Thus, the inclusion of quadratic crossshould be considered model dependent. Interestingly, the strips partition also improved the accuracy of logistic regres-sion although not as much as halves while quadrants did not.
This finding agreeswith the medical literature which has shown patients with AD tend to exhibit spa-tial neglect on one side of their visual field [14, 36, 37, 52, 53, 77]. Our system was also able to detect other known linguistic deficitsof AD patients, namely that they tend to use personal pronouns and shorter wordsmore often than healthy counterparts. Our main negative finding was that discourse features do not improve classifi-cation accuracy across the five models we tested. This is likely due to the struc-ture of the CookieTheft description task.
Therefore there is less opportunity for a response to be coherent or not compared to healthy controls. We therefore conclude that while discourse featuresare not useful in discriminating dementia from controls on the CookieTheft testthey may be useful in longer and less structured narratives, such as the blog dataset discussed in Chapter 6.
In that context, a speaker has an opportunity to use a36larger set of discourse relations to connect one statement to the next.
Feature importance score is calculated by equation 4. Feature ranking does not depend on any particularmodel and only is based on the correlation between the feature and thebinary labels. Mean word length, age, and noun phrase to personalpronoun are the highest scoring features on the DementiaBank data set. For each of the new feature sets we show the mean F-measureacross five models. Strips improves logistic regres-sion as well, to 0.
Quarters and Discourse have negligible effect on the perfor-mance of the best classifier. For each of the new feature sets we show the change in mean F-measure across five models when the new feature set is added. Whilehalves improves the performance of the best classifier logistic regres-sion it has mixed results on the suboptimal classifiers.
Large errorbars indicate the change in performance varies quite drastically betweenfolds. Discourse features have no effect. Feature importance score is calculated as shown in equation 4. Rightside receives an almost perfect score, scor-ing more highly than Mean word length, age, and Noun Phrase ToPersonal Pronoun from Figure 4.
Three other halves features, Con-centration: Box plots of the top four features from Figure 4. Top leftshows right-side perceptivity, top right shows age, bottom left showsnoun phrase to person pronoun a measure of how often the patientuses personal pronouns , and bottom right is mean length of words. Those with dementia are less perceptive on the right side of their vi-sual field than controls, as well as being older and more likely to usepersonal pronouns and shorter words. Given that it is amore heterogenous condition and is associated with less impairment than AD, peo-ple with MCI may not receive medical attention until they develop a more profoundcognitive impairment.
Given thatpeople with MCI have a greater potential benefit from further assessment and ther-apy than those who have progressed to dementia, a model that could make optimaluse of limited available data could be potentially very useful. This chapter will demonstrate how domain adaptation can be used to exploitavailable AD data, thereby improving detection of MCI from spoken language sam-ples. These algorithms arediscussed in detail in Section 5.
Our work differs from the previ-ous work on MCI described in Section 2. We use the feature setproposed by Fraser et al. Unlike Roark et al. Most sig-nificantly, the goal of our study was different: We beginwith a brief discussion of domain adaptation. This is typically done whenthe target domain has little or no labelled data, while the source domain has a rela-tively large amount of labelled data, as well as existing models trained on that data.
Typically the source data have been annotated for some phenomenon of interest,and the target data relate to another phenomenon that is very similar. The issue of domain adaptation has received increasing attention in recentyears. In work by Chelba and Acero , the source model is used to derive priorsfor the weights of the target model.
They employ this technique with a maximumentropy model and apply it to the task of automatic capitalization of uniformly-cased data.
Daume  introduced an approach wherein each feature is copied so thatthere is a source version, a target version and a general version of the feature.
Heshowed that this straight-forward approach could yield improvement on a varietyof NLP sequence labeling problems, such as named entity recognition, shallow44parsing and POS tagging.
More recently, Sun et al. We have implemented these two approaches, and describe them in more detail inbelow. We create three copies of each column: This augmented dataset is then fed into a standard learning algorithm. The motivation for this transformation is intuitive.
If a column contains a fea-ture such as mean word length which correlates to a diagnosis in both the targetand source data i. MCI and AD , a learning algorithm will increase the weight inthe common column and reduce the weight on target-only and source-only copies,thereby reducing their importance in the model. By expanding thefeature space and padding with zeros, a model can learn whether to apply a givenfeature on zero, one, or both data sets. Because of this we expect that models that clas-sify without learning weights e.
The algorithm first normalizes the source data to zeromean and unit variance, and then a whitening transform is performed on the sourcedata to remove the correlation between the source features. A whitening transformis a linear transformation of the feature space such that the covariance of the trans-formed feature space is the identity matrix.
We use PCA whitening on the sourcedata as follows: Then we set the whitening matrix to be the eigenvaluedecomposition with the negative square root of the eigenvalues. This results in the46whitened source data having an identity covariance: A model is then trained on the re-coloured source data and used to classify the target data.
In a the source dataand target data are normalized to unit variance and zero mean, but havedifferent covariances distributions. A classifier is then trained on the re-aligned source data. Figure adaptedfrom  5. Multiple inter-views from a single control patient were contained to either the target or the sourcedata sets, but not both. We compare against three do-main adaptation baselines. Target only trains the model only using target data,source only trains a model only using source data but evaluates on the target data.
In the relabeled source model, we pool the target data and source data in the train-ing folds and relabel AD to MCI. Along with the domain adaptation baselines we48included one baseline model, majority class, which predicts the majority class inthe training fold.
The test set contained only MCI data. Our goal wasto verify whether the accuracy achieved by using these domain adaptation methodsoutperforms the accuracy achieved by using MCI data alone. Onlytarget data appears in the test fold. CORAL does notimprove either model beyond the simple majority class baseline model, and forlogistic regression it results in a worse performance than the target only domainadaptation baseline. This underscores the importanceof only using the AUGMENT method with a model that is able to select, via theweight vector, which of the three copies of the feature to use.
AUGMENT requires a simple modification of the target and source feature space,and can be easily extended to incorporate source data from multiple domains. This is an important caveat that was not explicitly stated in the original pa-per by . Info-units, which we see in figures 4. This data is expensive to collect and does not accu-rately reflect how patients use language in daily life. There were million blogs on the web in , only twenty years since thefirst website was launched in .
As these bloggers enter their senescence,a percentage of them will be diagnosed with dementia, and a percentage of thosewill continue to use the internet. There will therefore be a growing data set avail-able in the form of tweets, blog posts, and social media comments with which totrain a classifier.
Provided these writers have a verified clinical diagnosis of de-mentia, such a data set would be large, inexpensive to acquire, easy to process,and require no manual transcriptions. This therefore mightmake it possible to detect subtle lexical, grammatical or pragmatic issues that maybe missed from spoken text.
There are downsides to using written language samples as well. Unlike spoken52language, written text can be edited or revised by oneself or others.
Thus, written samples may be biased towards more intactlanguage. Furthermore, researchers do not have an audio recording to accompanythe text and patients are not constrained to a single topic; people with dementiamay have greater facility discussing familiar topics.
A non-standardized data setwill also prevent the collection of common test-specific linguistic features such asinfo-units. However, working with a very large data set may be able to mitigate theeffects of these limitations.
Additionally, since substantial amounts of data can becollected for the same person, more accurate, user-specific longitudinal predictionsmight be possibleIn this chapter we present the first attempt at automatically detecting whethera blog post was written by an individual with dementia. We followed the generalmethodology described in Chapter 3 with a different data set, described in Sec-tion 6. The goal was to determine if this task is possible given the constraintslisted above, and also to determine if the features most discriminating in the writ-ten case are the same as in the spoken case.
We make our data set publicly availableat https: Three blogs were written by persons with dementia First blogger: Male,Dementia with Lewy Bodies, age 65 and three were written by family membersof persons with dementia to be used as control all female, ages unknown.
Otherdemographic information, such as education level, was unavailable. From each ofthe three dementia blogs, we manually filtered all texts not written by the ownerof the blog such as fan letters or posts containing more images than text.
Thisleft with samples written by persons with dementia and from healthycontrols. Control blogs were written by children, spouses, or caregivers of seniorswith dementia and were selected to control for topic and previous level of writingexperience.
Blog Information as of April 4th, We use the features described in Sec-tion 3. In total we extract features from each blog post witha binary label, indicating whether or not the author has dementia. We performeda 9-fold cross validation across all pairs of blogs with opposite labels. Each testfold contains all posts from one dementia blog and one control blog, and the postsfrom the remaining four blogs are used in the training fold.
As with the previousexperiments we run a feature selection step within each training fold, as describedin Section 3. Unlike with DementiaBank all models reachnear-optimal performance near 10 features then the performance either levels offor improves slightly as more features are added. All54models beat the baseline AUC of 0. We ran the same ablation analysis on the blogs data set as we performed on theDementiaBank Section 4.
The results are shown in Figure 6. Unlike with theDementiaBank data set psycholinguistic features are the most important featuregroup, with their ablation causing the performance of all models to drop signifi-cantly.
Somewhat unexpectedly the removal of the other feature groups causes aslight improvement in the best classifier, KNN, although the improvement is withinthe error bars in all cases, and not seen in logistic regression, the near-optimalclassifier. SUBTL word score, which is a measure of how frequently a word is used in dailylife, is the most highly correlated with a dementia diagnosis across all 9 folds. Thenumber of sentences per post also is highly correlated with a diagnosis.
As withthe DementiaBank data set, both mean word length and noun phrase to personalpronoun also score highly. We also observe that the error bands are larger for mostfeatures than in the figure 4. SUBTL word score, number of sentences, mean word length, noun phrase to per-sonal pronoun. SUBTL is a measure of how frequently aword is used in daily life, with a higher score indicating a more ordinary word anda lower score indicating a less common one.
Scores are derived from television andfilm subtitles. In the six blogs in our data set, bloggers with dementia use more or-dinary i.
Theyalso tend to write shorter blog posts, and in agreement with the DementiaBank dataset, use shorter words and more personal pronouns. We use two plots so error bars are distinguishable. As with figure 4. The number of features within each group are listed inparenthesis after each name group name.
Unlike with the Dementia-Bank data set all feature groups are important to the prediction accu-racy, with the removal of the psycholinguistic group having the greatestdeleterious effect across all models. Feature importance score for the blog data set, as calculated byequation 4.
Feature ranking does not depend on anyparticular model and only is based on the correlation between the featureand the binary labels. Box plots of the four highest scoring features in figure 6. Blogs written by persons with dementia are red and controls are blue. As in the spoken case, persons with dementia use the personal pronounmore often and use smaller words on average. Bloggers with dementiaalso have a higher SUBTL score indicating an impoverished vocabu-lary and write shorter posts.
We collected a data set of blog posts writtenby either persons with dementia or family members of persons with dementia. Wethen extracted lexical features from each post and evaluated the performanceof five classifiers in detecting whether the author of a post from an unseen blog hasdementia.
We also observed that bloggers with dementia tend to use fewer uncommon60words as indicated by the higher SUBTL score , and write shorter posts usingshorter words, on average. This finding is interesting because it would be difficultfor a human reading a single post to detect the simplified language provided thepost was coherent, which from inspection all were , but a higher SUBTL score andshorter word length could be detected automatically given a collection of posts. For example, we detectedan increased pronoun usage by bloggers with dementia compared to controls.
This opens the door to making use of the upcomingdeluge of online text written by seniors suffering from cognitive decline as data onwhich to train machine learning models. There have recently beensuccesses in using machine learning and natural language processing techniquesto automatically detect dementia from speech.
This thesis has made three maincontributions towards this effort. First, we proposed a novel set of features biologically motivated that we callspatial neglect features. These features measure whether the respondent is moreperceptive on one side of their visual field than the other.
We showed their in-clusion increases the F-measure of logistic regression from Thisachieves a new state of the art on the DementiaBank data set, beating the previousstate-of-the-art of We considered three different partitions of the Cooki-eTheft image, halves, strips, and quarters and found that halves performs best, inagreement with previous finding in medical literature.
Previous work has foundthat patients with AD show differences in discourse structure and so we also evalu-ated the effect of discourse features on model performance, but found they had noeffect on the DementiaBank data set. Second, we demonstrated how a simple domain adaptation algorithm can beused to overcome the lack of available mild cognitive impairment MCI data.
Last, we evaluated our framework on written data in the form of blog posts. It isnot obvious that a system that can detect dementia from spoken language could dothe same for written language, given that one can make revisions to text but cannotdo so for recorded extemporaneous speech.
We show that a range of models canpredict whether the author of a blog post has dementia at a rate far above baselines. Additionally we make the blog corpus used in our experiments publiclyavailable for future researchers. Besides the main contributions listed above, we made some observations thatwill be useful to practitioners and help guide future work.
For practitioners, werecommend the use of spatial neglect features with a halves partition as theyincreases the performance of most models, but recommend including them bothwith and without quadratic terms in order to determine which performs best. Inthe case of logistic regression, the addition of the quadratic terms improved theperformance significantly but for Random Forests and Gaussian Naive Bayes, thequadratic terms hurt the performance significantly.
This is likely due to the factthat many of the quadratic features were uninformative and some models are lesscapable of dealing with an excess of uninformative features than others1.
Asnoted in Section 5. Modelswhich are unable to do so, such as Random Forests, Gaussian Naive Bayes, andKNN are negatively impacted by the augmentation of the feature space. We also reiterate that practitioners should consider whether a substantial por-tion of their features are sparse or binary before using CORAL.
Similarly, the Naive Bayes classifier as-sumes conditional independence between all features, so including uninformative features e. We also sus-pect the poor performance is due to centering the features as a preprocessing step,as centering destroys sparsity. Wediscuss the spoken and written data sets separately for clarity.
Thisis done via a trivial extension to the standard feature space augmentation c. Incorporating source data from multiplepathologies could potentially improve our diagnostic capabilities, but this has yetto be shown. For example we couldpotentially improve upon the results in Chapter 4. In these settings discourse features,which Chapter 4 showed were not predictive on the DementiaBank data set, mayalso be more useful given the narrative structure of the speech samples.
There were a few limitations of our approach that we would like to address in futurework. First, the small size of our data set meant we were unable to differentiatebetween subtypes of dementia e. This isnot desirable because different pathologies have different symptoms cf.
We would like to collect a larger data set to allow us to control for types ofdementia, as well as demographic information such as age, gender, and educationlevel - information that was not present for all the blogs in our study. Another limitation of the above work is the unstructured nature of the text.
We couldpotentially improve our results by performing a topic clustering preprocessing stepon the blog posts. After clustering we could either train a classifier separately foreach cluster or include topic membership as a feature. Topic clustering would also help us to better understand the differences wefound in some linguistic markers between bloggers cf. Weobserved bloggers with dementia have a higher SUBTL score indicating an im-poverished vocabulary and shorter average word length compared to healthy con-trols.
These findings need further investigation to confirm if they are in fact dueto dementia-induced aphasia, as medical literature would predict. In Masrani et al. Results were inconclusive however, with the longitudinal trend of theSUBTL score moving in the direction opposite to what we expected. With topicclustering, we could track the longitudinal changes of certain linguistic markerswithin each topic, as well as the longitudinal changes in the topics themselves,to better understand the differences in writing style between the writers with andwithout dementia.
Finally, we hope to explore how aphasia manifests itself on different onlineplatforms. Language is shaped by its environment and linguistic features that areuseful in classifying blog posts may not be useful classifying tweets.
That number will surely rise as the Internetgeneration reaches adulthood and continues to use instant messenger, to commenton Facebook posts, and to converse on online forums. It therefore behooves us tounderstand how to detect signs of cognitive decline in these settings. Rhetorical structure and alzheimersdisease. Aphasiology, pages 1—20, Connectedspeech as a marker of disease progression in autopsy-proven alzheimersdisease. American Psychiatric Pub, The Cochrane Library, Domain adaptation with structuralcorrespondence learning.
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Technology use among seniors. Prevalence and monetarycosts of dementia in canada. The Alzheimer Society of Canada, American Journal of Speech-Language Pathology, 4 4: Adaptation of maximum entropy capitalizer: Littledata can help a lot.
Performanceon the rey-osterrieth complex figure test in alzheimer disease and vasculardementia. Cognitive and Behavioral Neurology, 12 2: Cutting the gordian knot: Themoving-average type—token ratio mattr. Journal of QuantitativeLinguistics, 17 2: Brain and language, 53 1: Frustratingly easy domain adaptation. So, you had two sisters, right?
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Journal of Applied Biomedicine, 14 2: Journal of Drug Delivery andTherapeutics, 3 3: Age-of-acquisition ratings for 30, english words. Behavior ResearchMethods, 44 4: The mini mental state examination mmse. Coherence andinformativeness of discourse in two dementia types.
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Dementia andgeriatric cognitive disorders, 29 4: The Gerontologist, 37 2: Ebixa also known as memantine hydrochloride. Number of blogs worldwide from to in millions. Harvard review ofpsychiatry, 23 5: Return of frustratingly easy domainadaptation. Speakingin alzheimers disease, is that an early sign? Frontiers in aging neuroscience, 7, Assessment of language function indementia. Memantine treatment in patients with moderateto severe alzheimer disease already receiving donepezil: Automatic detection of mild cognitiveimpairment from spontaneous speech using asr.
List of all features. Plot showing the performance of the halves feature set withoutquadratic terms. The performance of Random Forest and GaussianNaive Bayes is not hurt in this case as it is in figure 4. The perfor-mance of logistic regression also decreases without the quadratic terms. Accuracy of models with new feature sets. Change in accuracy of models with new feature sets. AUC of models with new feature sets. Change in AUC of models with new feature sets. Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
Include Metadata Specify width in pixels leave blank for auto-width: Our image viewer uses the IIIF 2. To load this item in other compatible viewers, use this url: Detecting dementia from written and spoken language. University of British Columbia. This thesis makes three main contributions to existing work on the automatic detection of dementia from language. First we introduce a new set of biologically motivated spatial neglect features, and show their inclusion achieves a new state of the art in classifying Alzheimer's disease AD from recordings of patients undergoing the Boston Diagnostic Aphasia Examination.
Second we demonstrate how a simple domain adaptation algorithm can be used to leveraging AD data to improve classification of mild cognitive impairment MCI , a condition characterized by a slight-but-noticeable decline in cognition that does not meet the criteria for dementia, and a condition for which reliable data is scarce.
Third, we investigate whether dementia can be detected from written rather than spoken language, and show a range of classifiers achieve a performance far above baseline.
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